​This session combines between new trends in learning algorithms within data mining and machine learning. The first one is the MapReduce programming paradigm which is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use. The second trend is about web security such as detecting phishing which is simply identifying the type of a website and email based on certain characteristics that are connected with the website/email. Papers related to distributed, parallel, incremental learning algorithms for the different type of data mining tasks (classification, clustering, association rule, etc) are welcome.

Themes

Learning algorithms related to MapReduce

Data Transformation Methods​

Distributed Learning algorithms

Parallel Learning algorithms

Incremental learning

Web Security Methods​

Email Security Methods​

Phishing Detection Methods

Learning Methods related to Phishing

Distributed Association Rule

Distributed Classification

Distributed Clustering

Scientific Committee

Fadi Thabtah

Peter Cowling

Yonghong Peng

Eyad Elyan

Lee McClusky

Joan Lu

Rami Mohammad

Aladdin Ayesh

Ayman Issa

Fredric Stahl

Wael Hadi